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Sentiment Analysis using Capsules Network

Katmusare, Shubham Siras (2021) Sentiment Analysis using Capsules Network. Masters thesis, Dublin, National College of Ireland.

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Abstract

One of the most important industries on the internet is online merchandise. Nowadays, much of the trade for various products is done online. It is self-evident to conclude that year after year, the internet business’s consumer base grows. From the previous year, the number of customers has exploded as a result of the pandemic. In order to meet the needs of the local client base, It is critical to look after the product in the most effective way possible in terms of the quality and the customer satisfaction index for the selling products. The level of quality that these firms are dealing with, as well as their comments, for the items that have been acquired by consumers can lead the organizations to deal with this issue.The purpose of this study is to determine the quality of software goods offered on the online platform ”Amazon” as well as consumer satisfaction with product transactions. Sentiment analysis with capsule networks was used to establish the product quality categorization. Based on user feedback, the classification model worked effectively in categorizing product quality as ”Good” or ”Bad.” The study’s findings may be used to get business insights on customer happiness and product quality for a certain product category. The capsule network implemented for the sentiment analysis acquired 70.47 percent classification accuracy.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Deep Neural networks; Capsule networks; Sentiment Analysis
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Online Shopping
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Online Shopping
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 06 Dec 2021 11:05
Last Modified: 06 Dec 2021 11:05
URI: http://norma.ncirl.ie/id/eprint/5174

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